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Spatial Patterns in Timing of the Diurnal Temperature Cycle : Volume 17, Issue 10 (01/10/2013)

By Holmes, T. R. H.

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Book Id: WPLBN0004010879
Format Type: PDF Article :
File Size: Pages 12
Reproduction Date: 2015

Title: Spatial Patterns in Timing of the Diurnal Temperature Cycle : Volume 17, Issue 10 (01/10/2013)  
Author: Holmes, T. R. H.
Volume: Vol. 17, Issue 10
Language: English
Subject: Science, Hydrology, Earth
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Hain, C., H. Holme, T. R., & Crow, W. T. (2013). Spatial Patterns in Timing of the Diurnal Temperature Cycle : Volume 17, Issue 10 (01/10/2013). Retrieved from

Description: USDA-ARS Hydrology and Remote Sensing Lab, Beltsville, MD, USA. This paper investigates the structural difference in timing of the diurnal temperature cycle (DTC) over land resulting from choice of measuring device or model framework. It is shown that the timing can be reliably estimated from temporally sparse observations acquired from a constellation of low Earth-orbiting satellites given record lengths of at least three months. Based on a year of data, the spatial patterns of mean DTC timing are compared between temperature estimates from microwave Ka-band, geostationary thermal infrared (TIR), and numerical weather prediction model output from the Global Modeling and Assimilation Office (GMAO). It is found that the spatial patterns can be explained by vegetation effects, sensing depth differences and more speculatively the orientation of orographic relief features. In absolute terms, the GMAO model puts the peak of the DTC on average at 12:50 local solar time, 23 min before TIR with a peak temperature at 13:13 (both averaged over Africa and Europe). Since TIR is the shallowest observation of the land surface, this small difference represents a structural error that possibly affects the model's ability to assimilate observations that are closely tied to the DTC. The equivalent average timing for Ka-band is 13:44, which is influenced by the effect of increased sensing depth in desert areas. For non-desert areas, the Ka-band observations lag the TIR observations by only 15 min, which is in agreement with their respective theoretical sensing depth. The results of this comparison provide insights into the structural differences between temperature measurements and models, and can be used as a first step to account for these differences in a coherent way.

Spatial patterns in timing of the diurnal temperature cycle

Anderson, M. C., Norman, J. M., Diak, G. R., Kustas, W. P., and Mecikalski, J. R.: A two-source time-integrated model for estimating surface fluxes using thermal infrared remote sensing, Remote Sens. Environ., 60, 195–216, 1997.; Anderson, M. C., Hain, C., Wardlow, B., Pimstein, A., Mecikalski, J. R., and Kustas, W. P.: Evaluation of drought indices based on thermal remote sensing of evapotranspiration over the continental United States, J. Climate, 24, 2025–2044, doi:10.1175/2010jcli3812.1, 2011.; Bauer, P., Auligné, T., Bell, W., Geer, A., Guidard, V., Heilliette, S., Kazumori, M., Kim, M. J., Liu, E. H. C., and McNally, A. P.: Satellite cloud and precipitation assimilation at operational NWP centres, Q. J. Roy. Meteor. Soc., 137, 1934–1951, 2011.; Betts, A. K. and Ball, J. H.: The FIFE surface diurnal cycle climate, J. Geophys. Res.-Atmos., 100, 25679–25693, 1995.; Bosilovich, M. G., Radakovich, J. D., da Silva, A., Todling, R., and Verter, F.: Skin temperature analysis and bias correction in a coupled land-atmosphere data assimilation system, J. Meteorol. Soc. Jpn., 85, 205–228, 2007.; Choudhury, B. J., Idso, S. B., and Reginato, R. J.: Analysis of an empirical model for soil heat flux under a growing wheat crop for estimating evaporation by an infrared-temperature based energy balance equation, Agr. Forest Meteorol., 39, 283–297, 1987.; Colwell, R. N., Simonett, D. S., and Ulaby, F. T. (Eds.): Manual of remote sensing, in: Interpretation and Applications, 2nd Edn., Vol. II, Falls Church, 1983.; Cornwall, C., Horiuchi, A., and Lehman, C.: General solar position calculations, available at: (last access: May 2013), 2003.; Dai, A. and Trenberth, K. E.: The diurnal cycle and its depiction in the Community Climate System Model, J. Climate, 17, 930–951, doi:2.0.CO;2>10.1175/1520-0442(2004)017<0930:TDCAID>2.0.CO;2, 2004.; Duan, S.-B., Li, Z.-L., Wang, N., Wu, H., and Tang, B.-H.: Evaluation of six land-surface diurnal temperature cycle models using clear-sky in situ and satellite data, Remote Sens. Environ., 124, 15–25, 2012.; Ducharne, A., Koster, R., Suarez, M., Stieglitz, M., and Praveen, K.: A catchement-based approach to modeling land-surface processes in a GCM – 2. Parameter estimation and model demonstration, J. Geophys. Res., 105, 24809–24822, 2000.; Fiebrich, C. A., Martinez, J. E., Brotzge, J. A., and Basara, J. B.: The Oklahoma Mesonet's Skin Temperature Network, J. Atmos. Ocean. Tech., 20, 1496–1504, doi:2.0.CO;2>10.1175/1520-0426(2003)020<1496:TOMSTN>2.0.CO;2, 2003.; Göttsche, F.-M. and Olesen, F. S.: Modelling of diurnal cycles of brightness temperature extracted from METEOSAT data, Remote Sens. Environ., 76, 337–348, 2001.; Göttsche, F.-M. and Olesen, F.-S.: Modelling the effect of optical thickness on diurnal cycles of land surface temperature, Remote Sens. Environ., 113, 2306–2316, 2009.; Hain, C. R., Anderson, M. C., Zhan, X., Svoboda, M., Wardlow, B., Mo, K., Meckalski, J. R., Kustas, W. P., and Brown, J.: A GOES Thermal-Based Drought Early Warning Index for NIDIS, in: NOAA's National Weather Service, Science and Techn. Inf. Climat. B., Fort Worth, TX, 2011.; Holmes, T. R. H., De Jeu, R. A. M., Owe, M., and Dolman, A. J.: Land surface temperature from Ka band (37 GHz) passive microwave observations, J. Geophys. Res., 114, D04113, doi:10.1029/2008JD010257, 2009.; Holmes, T. R. H., Jackson, T. J., Reichle, R. H., and Basara, J. B.: An assessment of surface soil temperature products from numerical weather prediction models using ground-based measurements, Water Resour. Re


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